An organization may wish to collect, store, monitor, and analyze data. As the amount of data grows, data amalgamation mechanisms are increasingly relied upon in order to form organizational and corporate strategic decisions. While current data assembly mechanisms may allow for collection of raw data, there exist some shortcomings. For example, collected raw data may not be readily usable, and may need to be modified or summarized prior to analysis or synthesis. Moreover, collected data may constitute low quality data, and errors may occur during collection and/or transformation of data. For example, errors related to data quality may include incomplete data (where data has not been pulled from one source), incorrect transformations to a data element, wrong manual entry of the data, and errors in calculations. Further, at present it is difficult to assess data quality for a large dataset and focus is usually on individual tables of data. With such errors, difficulties in assessment, and without such transformation, premature use of data for analysis may cause poor organizational decision-making resulting in significant monetary costs and reputational damage to a company.
Furthermore, while current data quality tools allow for processing of significant amounts of data, existing tools may not provide comprehensive means for data exploration and monitoring. For example, some data platforms may be used solely to execute processes related to collection at the individual dataset level, while other tools may be used solely to execute processes related to data tracking. Additionally, many data quality tools require manual implementation, which may be tiresome and burdensome to operate since there exists no standardized procedure. Use of disaggregated and manual data collection mechanisms across multiple platforms may also result in tedious or erroneous data analysis. Furthermore, where data monitoring for a large number of variables is required, use of existing data quality tools may require significant human capital over a long period of time and at significant cost to an organization.
Accordingly, it may be desirable to provide a standard data quality process or rule-based workflow implementable within a singular platform. This process may significantly distinguish from or improve over a manual process. For example, where an example variable is an annual percentage rate (APR), manual rules may check whether the contract APR is a number and whether it is greater than zero. However, the enhanced process described herein may include the creation of suggested rules, including not only a rule to check whether contract APR is a number greater than zero, but also including a rule indicating that the contract APR should not be missing if the contract is finalized. This rule provides a further check, thus improving data quality over that of manual rule implementation. This provides an improvement in data quality and an efficiency gain.
Additionally, an automated end-to-end data application may be preferable in order to allow for streamlined data procurement, analysis using consistent metrics, and monitoring. Moreover, there exists a need for a user-intuitive point-and-click interface allowing for rapid and efficient monitoring and exploration of significant quantities of data sets and elements. Further, there exists a need for a comprehensive data tool which allows a user to perform diagnosis of data quality directly within the data tool. Current platforms are inefficient, difficult, or even impossible, thus requiring excess operator time and processing resources. Further, typical processes for managing data quality are subjective and not automated. Such processes are time- and resource-consuming. Therefore, it is desirable to implement a distinctly computer-implemented and enhanced automated process which improves the management of data quality.
The present disclosure is directed at addressing one or more of the shortcomings set forth above and/or other problems of existing hardware systems.